The making of ideal immigrant settlement workers: examining the technologies of ruling power in Canadian immigrant service organisations
Bibliographic record
Abstract
Underpinned by neoliberal rationalities, the contractual relationship between government funders and immigrant-serving organisations (ISOs) has led these agencies to promote neoliberal values of competition in the business market, prioritising quantity over quality in their services in order to secure government funding. Informed by Foucault's concept of governmentality as its theoretical framework and institutional ethnography (IE) as its methodology, our study investigates the work experiences of 18 immigrant settlement workers (ISWs) at three ISOs in western Canada. This study identifies how following an outcomes-driven evaluation approach, as required by the federal government, produces a series of textually mediated accountabilities, constructing translocal textual social relations that further coordinate and govern ISWs’ conduct in their local ISO workplaces. This evaluation approach, as analyzed in our study, is exercised as the technologies of ruling power, which is strengthened by the ruling of systems, workplace knowledge, social relations, and the governed-self, producing ideal ISWs who are self-accountable, self-regulated, adaptable, and productive. This process of making ideal ISWs legitimises ISWs’ apparatus role in reinforcing technologies of ruling power from the individual, organisational and institutional perspectives to better serve the agenda of the state.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.028 | 0.047 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".